Background: A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

Results: In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

Conclusion: Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

Figure 4: The pheromone response signaling pathway. (A) The main chain of the pheromone pathway downloaded from KEGG, (B) the output of our PathFinder implementation, (C) The color-coding algorithm output for the pheromone pathway [20], (D) the NetSearch program prediction for the pheromone pathway [18]. The interactions that do not exist in the PPIN are shown with dashed edges in (A). Notice that, for (B),(C) and (D) the proteins that were not on the main chain of the pathway, as shown in (A), were not colored.

Mentions:
Now we analyze the performance of PathFinder on some particular signaling pathways. Pheromone response and filamentous growth pathways were analyzed previously in [18,20]. We used Pathfinder to recover these specific pathways as well. The pheromone response pathway (Figure 1) triggers the yeast cell for mating by inducing polarized cell growth toward a mating partner. This pathway consists of ten cascading proteins with additional proteins assisting or binding on the sides. First, the pathway segment was excluded from the training data set. Given the yeast PPIN and the starting and ending proteins of the pheromone response pathway (Ste2-Ste12), PathFinder (without inferred links) generated the pathway shown in Figure 4B. However, note that the PPIN that is currently available is known to be incomplete. When the yeast PPIs are compared with the pheromone response pathway interactions [30], the interaction between Ste4/8 and Cdc42 does not exist in the yeast PPIN.

Figure 4: The pheromone response signaling pathway. (A) The main chain of the pheromone pathway downloaded from KEGG, (B) the output of our PathFinder implementation, (C) The color-coding algorithm output for the pheromone pathway [20], (D) the NetSearch program prediction for the pheromone pathway [18]. The interactions that do not exist in the PPIN are shown with dashed edges in (A). Notice that, for (B),(C) and (D) the proteins that were not on the main chain of the pathway, as shown in (A), were not colored.

Mentions:
Now we analyze the performance of PathFinder on some particular signaling pathways. Pheromone response and filamentous growth pathways were analyzed previously in [18,20]. We used Pathfinder to recover these specific pathways as well. The pheromone response pathway (Figure 1) triggers the yeast cell for mating by inducing polarized cell growth toward a mating partner. This pathway consists of ten cascading proteins with additional proteins assisting or binding on the sides. First, the pathway segment was excluded from the training data set. Given the yeast PPIN and the starting and ending proteins of the pheromone response pathway (Ste2-Ste12), PathFinder (without inferred links) generated the pathway shown in Figure 4B. However, note that the PPIN that is currently available is known to be incomplete. When the yeast PPIs are compared with the pheromone response pathway interactions [30], the interaction between Ste4/8 and Cdc42 does not exist in the yeast PPIN.

Bottom Line:
Our goal is to find biologically significant pathway segments in a given interaction network.Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives).In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

Background: A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

Results: In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

Conclusion: Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.